Notes on tensorflow（八）read tfrecords with slim

http://blog.csdn.net/weixin_35653315/article/details/71015845 中描述了将pascal voc数据集转换tfrecord的过程。本文则通过slim来读取生成的tfrecord。读取和解码操作由

tf.TFRecordReader

完成

import tensorflow as tf
slim = tf.contrib.slim
file_pattern = './pascal_train_*.tfrecord' #文件名格式

# 适配器1：将example反序列化成存储之前的格式。由tf完成
keys_to_features = {
'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
'image/format': tf.FixedLenFeature((), tf.string, default_value='jpeg'),
'image/height': tf.FixedLenFeature([1], tf.int64),
'image/width': tf.FixedLenFeature([1], tf.int64),
'image/channels': tf.FixedLenFeature([1], tf.int64),
'image/shape': tf.FixedLenFeature([3], tf.int64),
'image/object/bbox/xmin': tf.VarLenFeature(dtype=tf.float32),
'image/object/bbox/ymin': tf.VarLenFeature(dtype=tf.float32),
'image/object/bbox/xmax': tf.VarLenFeature(dtype=tf.float32),
'image/object/bbox/ymax': tf.VarLenFeature(dtype=tf.float32),
'image/object/bbox/label': tf.VarLenFeature(dtype=tf.int64),
'image/object/bbox/difficult': tf.VarLenFeature(dtype=tf.int64),
'image/object/bbox/truncated': tf.VarLenFeature(dtype=tf.int64),
}

#适配器2：将反序列化的数据组装成更高级的格式。由slim完成
items_to_handlers = {
'image': slim.tfexample_decoder.Image('image/encoded', 'image/format'),
'shape': slim.tfexample_decoder.Tensor('image/shape'),
'object/bbox': slim.tfexample_decoder.BoundingBox(
['ymin', 'xmin', 'ymax', 'xmax'], 'image/object/bbox/'),
'object/label': slim.tfexample_decoder.Tensor('image/object/bbox/label'),
'object/difficult': slim.tfexample_decoder.Tensor('image/object/bbox/difficult'),
'object/truncated': slim.tfexample_decoder.Tensor('image/object/bbox/truncated'),
}

# 解码器
decoder = slim.tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers)

# dataset对象定义了数据集的文件位置，解码方式等元信息
dataset = slim.dataset.Dataset(
data_sources=file_pattern,
reader=tf.TFRecordReader,
num_samples = 3, # 手动生成了三个文件， 每个文件里只包含一个example
decoder=decoder,
items_to_descriptions = {},
num_classes=21)

#provider对象根据dataset信息读取数据
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
num_readers=3,
shuffle=False)

[image, shape, glabels, gbboxes] = provider.get(['image', 'shape',
'object/label',
'object/bbox'])
print type(image)
print image.shape

<class 'tensorflow.python.framework.ops.Tensor'>
(?, ?, 3)

到目前为止，返回的image是一个tensor，而且是一个3维的，一次只有一张。需要组成一个batch。组成batch之前图片要预处理，一是为了将图片size变成固定大小， 二是数据增长。上面的示例代码来自https://github.com/balancap/SSD-Tensorflow/blob/master/datasets/pascalvoc_common.py#L49

。下面的示例代码来自 https://github.com/balancap/SSD-Tensorflow/blob/master/train_ssd_network.py#L203

# Pre-processing image, labels and bboxes.
image, glabels, gbboxes = \
image_preprocessing_fn(image, glabels, gbboxes,
out_shape=ssd_shape,
data_format=DATA_FORMAT)
# Encode groundtruth labels and bboxes.
gclasses, glocalisations, gscores = \
ssd_net.bboxes_encode(glabels, gbboxes, ssd_anchors)
batch_shape = [1] + [len(ssd_anchors)] * 3

# Training batches and queue.
r = tf.train.batch(
tf_utils.reshape_list([image, gclasses, glocalisations, gscores]),
batch_size=FLAGS.batch_size,
num_threads=FLAGS.num_preprocessing_threads,
capacity=5 * FLAGS.batch_size)
b_image, b_gclasses, b_glocalisations, b_gscores = \
tf_utils.reshape_list(r, batch_shape)

# Intermediate queueing: unique batch computation pipeline for all
# GPUs running the training.
batch_queue = slim.prefetch_queue.prefetch_queue(
tf_utils.reshape_list([b_image, b_gclasses, b_glocalisations, b_gscores]),
capacity=2 * deploy_config.num_clones)